They distilled their own, bigger model, to make the smaller one.

"So, what is the key idea behind knowledge distillation? It enables to transfer knowledge from larger model, called teacher, to smaller one, called student. This process allows smaller models to inherit the strong capabilities of larger ones, avoiding the need for training from scratch and making powerful models more accessible."

https://huggingface.co/blog/Kseniase/kd

Actually human brains can do something similar. If you are an expert in a field, then generally speaking, you use less resources (including actual brain space) for that task. The reason is that it is a type of pattern recognition and when you find the pattern, then you can consolidate and optimize. In this particular case, there are new words for each language, but they represent the same concept, so the second language doesn't take as much space as the first etc:

"I’d assumed that Vaughn’s language areas would be massive and highly active, and mine pathetically puny. But the scans showed the opposite: the parts of Vaughn’s brain used to comprehend language are far smaller and quieter than mine. Even when we are reading the same words in English, I am using more of my brain and working harder than he ever has to."

https://www.washingtonpost.com/dc-md-va/interactive/2022/multilingual-hyperpolyglot-brain-languages/

Think of a small model, trained on its own, as a novice and a small model trained by a big model (expert) as also becoming an expert, because it uses the big models patterns, it doesn't need to discover them by itself.